Machine learning to detect signatures of disease in liquid biopsies - a user's guide

被引:108
作者
Ko, Jina [1 ]
Baldassano, Steven N. [1 ]
Loh, Po-Ling [2 ]
Kording, Konrad [1 ,3 ]
Litt, Brian [1 ,4 ]
Issadore, David [1 ,5 ]
机构
[1] Univ Penn, Sch Engn & Appl Sci, Dept Bioengn, Philadelphia, PA 19104 USA
[2] Univ Wisconsin Madison, Dept Elect & Comp Engn, Madison, WI USA
[3] Univ Penn, Dept Neurosci, Perelman Sch Med, Philadelphia, PA 19104 USA
[4] Univ Penn, Dept Neurol, Perelman Sch Med, Philadelphia, PA 19104 USA
[5] Univ Penn, Sch Engn & Appl Sci, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA
关键词
CIRCULATING TUMOR-CELLS; PERIPHERAL-BLOOD; CANCER; EXOSOMES; CLASSIFICATION; HETEROGENEITY; DIAGNOSTICS; BIOMARKERS; ACCURACY; DNA;
D O I
10.1039/c7lc00955k
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in 'liquid biopsy' often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.
引用
收藏
页码:395 / 405
页数:11
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